Establishment and analysis of prediction model for invasive subsolid pulmonary nodules based on radiomics

Abstract

Objective: To evaluate the best radiomic features based prediction model for identifying the histopathological subtypes of invasive adenocarcinoma or noninvasive pulmonary nodules appearing as subsolid nodules. Methods: A total of 352 patients (108 males and 244 females, median age was [M(Q1,Q3)]57 (50,65), underwent high-resolution chest CT and appearing as subsolid nodules and further treated by surgical resection whose subsequently pathological results were classified as atypical adenomatous hyperplasia (AAH), carcinoma in situ (AIS), microinvasive carcinoma (MIA), invasive adenocarcinoma (IA), from January 2015 to September 2019, in Radiology Department of Zhongda Hospital Affiliated to Southeast University and Jinling Hospital, Medical School of Nanjing University were retrospectively collected. They were divided into non-invasive group (n=233) and invasive group (n=119) according to pathological findings. According to the ratio of training set: internal test set: external test set, which is about 3∶1∶1,the patients in Zhongda Hospital Affiliated to Southeast University were randomly divided into training set (n=215, non-IA∶IA 155∶60) and internal test set(n=69, non-IA∶IA 52∶17), meanwhile a certain number of patients in Jinling Hospital, Medical School of Nanjing University(n=68, non-IA∶IA 26∶42)were randomly selected as an independent external test set. Particular quantitative parameters of the nodules, radiomic features, morphological characteristics, clinical data, and serum tumor markers were recorded. Radiomic label was constructed using LASSO regression method. The morphological model, CT model and comprehensive model were constructed by binary logistic regression and were verified in test sets, respectively. Results: Shape_MinorAxis(Gradient),Glszm_ZoneEntropy(LBP) were selected as the two most significant features based on training set. Radiomic tag=1.065 75×Shape_MinorAxis(Gradient)+0.030 58×Glszm_ZoneEntropy(LBP). Comparing the prediction performance of all models in each data cohort, the CT model (Ln(P/1-P)=-2.417 11+1.031 60×Radimic tag+1.203 06×Diameter+1.614 21×(Pleural indentation sign = Y) constructed by radiomic label, pleural depression, and quantitative parameters (diameter, average density) was much better than other models and was chosen as the optimal model, with an AUC of CT models in training cohort and test cohort was 0.954 (95%CI: 0.927-0.981), 0.865 (95%CI:0.764-0.966), better than morphological model 0.857 (95%CI:0.796-0.918), 0.818(95%CI: 0.686-0.949) and comprehensive model 0.951(95%CI: 0.921-0.981), 0.856(95%CI: 0.730-0.982), respectively. Conclusion: The integrative CT model has a better prediction efficiency for identifying invasive or noninvasive nodules appearing as subsolid nodules.